The rate of depression among diabetics is almost twice as high as the general population. Subsequently, diabetics with comorbid depression have a greater chance of developing many of the complications and comorbidities associated with diabetes compared to the baseline diabetic population. This can include neuropathy, nephropathy, retinopathy, coronary heart disease, and cerebrovascular disease. Inevitably, this increased risk of developing these complications and comorbidities results in higher annual health care costs for diabetics with depression when compared to diabetics alone. Addressing the health care needs of this specific sub-population could prove beneficial in terms of slowing down disease progression and decreasing the health care dollars spent on treating this chronic disease that continues to afflict a sizeable portion of our population. Researchers previously have studied the health and economic benefits of treating depression using a 12-month stepped care therapy consisting of frequent monitoring, pharmacotherapy, and/or problem-solving therapy. These studies have found positive results, but are limited in their generalizability due t both short timeframes and specific study sample populations. Accordingly, the proposed research will be the first modeling study to use a state-transition simulation model to analyze the long-term health and economic benefits of treating depression among diabetics, filling a sizeable knowledge gap in the field. Along with evaluating the health and economic outcomes of this intervention, we will be using the model to vary intervention and population characteristics to identify both an optimal treatment strategy as well as the model inputs that are the main determinants of the results we find. Through this type of analysis, we expect to provide clinicians with evidence of the ways to improve the care of diabetics with depression so that these patients can achieve better outcomes in the short and long-term.